---
title: "Exploratory Analysis Dashboard"
output:
flexdashboard::flex_dashboard:
social: menu
source: embed
theme: flatly
---
```{r setup}
library(tidyverse)
library(leaflet)
library(lubridate)
library(ggExtra)
library(plotly)
library(maps)
library(mapdata)
library(ggthemes)
library(mapproj)
library(ggthemes)
library(gganimate)
library(viridis)
library(wordcloud)
library(RColorBrewer)
library(tm)
library(dplyr)
library(scales)
library(gganimate)
theme_set(theme_minimal() + theme(legend.position = "bottom"))
```
Trends in Time{.storyboard}
=========================================
### The Number of Crimes Over Six Years
```{r, echo=FALSE}
crime_full=
read.csv("/Users/yitian/Desktop/p8105_final_project/data/data_clean.csv")
count_overtime =
crime_full %>%
group_by(year) %>%
summarize(cases = n())
count_overtime %>%
plot_ly(x = ~year, y = ~cases, type = "scatter",mode = "lines+markers") %>%
layout(
title = "Figure 1: The number of different crimes trends over time in Chicago.",
xaxis = list(title = "Year"),
yaxis = list(title = "Total crime cases in Chicago")
)
```
***
The figure shows that the number of crimes in Chicago change over the most recent six years. The range of the number of crimes per year is between 4629 and 10824 from 2016 to 2021.
We could see that, the peak number of the crimes over time is 2020 with 10824 cases. Basically, the trend went smooth from 2016 to 2020 and went down after 2020.
Heat Map{.storyboard}
=========================================
### The trend of crime happens in different months and years
```{r, message=FALSE}
heatmap_plot = crime_full %>%
mutate(month = month.abb[as.numeric(month)],
month = fct_rev(factor(month, levels = month.abb))) %>%
group_by(year, month, day) %>%
summarise(n_crimes = n()) %>%
mutate(day = as.numeric(day))
heatmap_plot = heatmap_plot %>%
ggplot(aes(x = day, y = month, fill = n_crimes))+
geom_tile(color = "white",size = 0.1) +
scale_fill_viridis(name = "Number of Crimes",option = "C") +
facet_grid(.~ year) +
scale_x_continuous(breaks = c(1,10,20,31)) +
theme_minimal(base_size = 8) +
labs(title = "Figure 2: Number of Crimes from 2016 to 2021", x = "Day", y = "Month") +
theme(legend.position = "bottom")+
theme(plot.title = element_text(size = 14))+
theme(axis.text.y = element_text(size = 6)) +
theme(strip.background = element_rect(colour = "white"))+
theme(plot.title = element_text(hjust = 0))+
theme(axis.ticks = element_blank())+
theme(axis.text = element_text(size = 7))+
theme(legend.title = element_text(size = 8))+
theme(legend.text = element_text(size = 6))+
removeGrid()
ggplotly(heatmap_plot + theme(legend.position = "none"))
```
***
This heat map clearly shows that the crime rate has a significant increase in 2020, and the significant increase of crime in Chicago is probably associated with the COVID-19 prevalence. In 2021, the total crime rates is a little bit lower than previous year. In general, there is no significant differences on crime rates with diffeernt months.
Word Clouding{.storyboard}
=========================================
### Word Clouding of Crime Types: The top number of crimes appeared in 6 years
```{r, warning=FALSE, message=FALSE}
crime = read_csv("/Users/yitian/Desktop/p8105_final_project/data/data_clean.csv") %>%
group_by(primary_type) %>%
summarise(n_crime = n())
set.seed(555)
wordcloud(words = crime$primary_type, freq = crime$n_crime, scale = c(3, .8),min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
title( "Figure 3: Wordclouding of the top number of crimes during 6 years")
```
***
This word clouding figure shows the top number of crimes. According to this figure, Battery is the top crime which happens the most time in past 6 years. Battery means the intentional use of force or violence against another. Thus, people in Chicago need to pay more attention on others' intentionally physical harm.
Trends in Location{.storyboard}
=========================================
### The Number of Crimes Happens in Different Locations Over Six Years
```{r, warning=FALSE, message=FALSE}
## clean a new dataset with ranking of numbers and adjusted numbers
crime_loc = read.csv("/Users/yitian/Desktop/p8105_final_project/data/data_clean.csv") %>%
group_by(year,location_description) %>%
summarise(n = n()) %>%
mutate(rank = rank(-n),
Value_rel = n/n[rank == 1],
Value_lbl = paste0(" ",n)) %>%
filter(rank <= 10)
## form multiple static plots
staticplot = ggplot(crime_loc, aes(rank, group = location_description,
fill = as.factor(location_description), color = as.factor(location_description))) +
geom_tile(aes(y = n / 2,
height = n,
width = 0.9), alpha = 0.8, color = NA) +
geom_text(aes(y = 0, label = paste(location_description, " ")), vjust = 0.2, hjust = 1) +
geom_text(aes(y = n,label = Value_lbl, hjust = 0)) +
coord_flip(clip = "off", expand = FALSE) +
scale_x_reverse() +
scale_fill_viridis_d(option = "B") +
scale_color_viridis_d(option = "B") +
theme_minimal() +
theme(axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none",
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_line( size = .1, color = "grey" ),
panel.grid.minor.x = element_line( size = .1, color = "grey" ),
plot.title = element_text(size = 20, hjust = 0.5, face = "bold", vjust = 2),
plot.subtitle = element_text(size = 14, hjust = 0.5, face = "italic", color = "grey"),
plot.background = element_blank(),
plot.margin = margin(2,2, 2, 4, "cm"))
## convert static plots to animated ones
anim_plot = staticplot +
transition_states(year, transition_length = 4, state_length = 1) +
ease_aes('sine-in-out') +
labs(title = 'Figure 3: Number of Crime in Chicago from 2016 to 2021 : {closest_state}',
subtitle = "Top 10 Location")
animate(anim_plot, 150, fps = 15, width = 780, height = 560)
```
***
We also sorted and ranked the data, and compiled the top 10 locations with high criminal incidence from 2016 to 2021 into a dynamic bar graph. Through this motion chart, we can visually observe the names of the crime areas, the number of crime incidents, and their changing trends.
We can note that apartments, residence, streets and sidewalks are firmly in the top 4 from 2016 to 2020. They have been the locations with high crime rates during the six-year period. This result also coincides with our previous study of the types of crime that occur most frequently. The top three crime types in Chicago in our study are battery, assault, and other offenses, and these crime types are indeed more likely to occur in these locations.
It is also interesting to note that the number of crimes in apartment and residence had a large increase and reached a peak in 2020 and then dropped sharply to an average level in 2021, while other locations also experiencing a small decrease in crime during 2020. We believe that this trend may be related to the home quarantine policy for the Covid-19 epidemic in 2020.
